code: def test_uu_implicit():
      "Train and use user-user on an implicit data set."
      algo = knn.UserUser(20, feedback="implicit")
      data = ml_ratings.loc[:, ["user", "item"]]
  
      algo.fit(data)
      assert algo.user_means_ is None
  
      mat = algo.rating_matrix_.to_scipy()
      norms = spla.norm(mat, 2, 1)
      assert norms == approx(1.0)
  
      preds = algo.predict_for_user(50, [1, 2, 42])
      assert all(preds[preds.notna()] > 0)
  
imports: from lenskit.algorithms import Recommender
  import lenskit.algorithms.user_knn
  from lenskit.util import clone
  from pathlib import Path
  import logging
  import pickle
  import pandas
  import numpy
  from scipy.sparse import linalg
  from pytest import approx, mark
  import lenskit.util.test
label_desc: Use image node_id 13 for calling a global function with example usage: test_uu_implicit() without return types